Overview

Dataset statistics

Number of variables18
Number of observations500
Missing cells1104
Missing cells (%)12.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory70.4 KiB
Average record size in memory144.3 B

Variable types

Numeric11
Text2
Unsupported2
Categorical2
DateTime1

Alerts

id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with id and 1 other fieldsHigh correlation
latitude is highly overall correlated with longitude and 1 other fieldsHigh correlation
longitude is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
neighbourhood is highly overall correlated with host_id and 3 other fieldsHigh correlation
room_type is highly overall correlated with neighbourhoodHigh correlation
neighbourhood_group has 500 (100.0%) missing valuesMissing
last_review has 52 (10.4%) missing valuesMissing
reviews_per_month has 52 (10.4%) missing valuesMissing
license has 500 (100.0%) missing valuesMissing
host_id is highly skewed (γ1 = 22.05768788)Skewed
id has unique valuesUnique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
license is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 52 (10.4%) zerosZeros
availability_365 has 55 (11.0%) zerosZeros
number_of_reviews_ltm has 166 (33.2%) zerosZeros

Reproduction

Analysis started2023-11-01 11:27:45.668738
Analysis finished2023-11-01 11:28:32.377626
Duration46.71 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537761.78
Minimum17878
Maximum942218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:32.611624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum17878
5-th percentile89891.45
Q1286396.25
median521684
Q3829032
95-th percentile919868.05
Maximum942218
Range924340
Interquartile range (IQR)542635.75

Descriptive statistics

Standard deviation286024.48
Coefficient of variation (CV)0.53187951
Kurtosis-1.4203727
Mean537761.78
Median Absolute Deviation (MAD)261642
Skewness-0.078948901
Sum2.6888089 × 108
Variance8.1810001 × 1010
MonotonicityStrictly increasing
2023-11-01T08:28:33.146658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17878 1
 
0.2%
758632 1
 
0.2%
769674 1
 
0.2%
768172 1
 
0.2%
765614 1
 
0.2%
765255 1
 
0.2%
764769 1
 
0.2%
764534 1
 
0.2%
764205 1
 
0.2%
762988 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
17878 1
0.2%
25026 1
0.2%
35764 1
0.2%
48305 1
0.2%
48901 1
0.2%
49179 1
0.2%
51703 1
0.2%
53533 1
0.2%
60718 1
0.2%
64795 1
0.2%
ValueCountFrequency (%)
942218 1
0.2%
941735 1
0.2%
940749 1
0.2%
937388 1
0.2%
935162 1
0.2%
935090 1
0.2%
935028 1
0.2%
932270 1
0.2%
931638 1
0.2%
930736 1
0.2%

name
Text

Distinct421
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:33.512725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length83
Median length74
Mean length64.97
Min length41

Characters and Unicode

Total characters32485
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)75.8%

Sample

1st rowCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath
2nd rowRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed · 1 bath
3rd rowLoft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 baths
4th rowRental unit in Ipanema · ★4.74 · 6 bedrooms · 7 beds · 7 baths
5th rowRental unit in Rio · ★4.37 · 4 bedrooms · 5 beds · 3 baths
ValueCountFrequency (%)
· 1897
23.3%
1 793
 
9.8%
in 500
 
6.1%
rio 470
 
5.8%
de 440
 
5.4%
janeiro 440
 
5.4%
rental 384
 
4.7%
unit 384
 
4.7%
2 330
 
4.1%
bedroom 311
 
3.8%
Other values (138) 2183
26.8%
2023-11-01T08:28:34.773757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7635
23.5%
e 2442
 
7.5%
o 2036
 
6.3%
· 1897
 
5.8%
i 1858
 
5.7%
n 1771
 
5.5%
d 1529
 
4.7%
a 1516
 
4.7%
b 1480
 
4.6%
t 1362
 
4.2%
Other values (45) 8959
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17837
54.9%
Space Separator 7635
23.5%
Decimal Number 2741
 
8.4%
Other Punctuation 2382
 
7.3%
Uppercase Letter 1474
 
4.5%
Other Symbol 413
 
1.3%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2442
13.7%
o 2036
11.4%
i 1858
10.4%
n 1771
9.9%
d 1529
8.6%
a 1516
8.5%
b 1480
8.3%
t 1362
7.6%
r 1030
5.8%
s 747
 
4.2%
Other values (15) 2066
11.6%
Uppercase Letter
ValueCountFrequency (%)
R 854
57.9%
J 441
29.9%
H 53
 
3.6%
C 41
 
2.8%
S 35
 
2.4%
L 18
 
1.2%
T 8
 
0.5%
G 8
 
0.5%
B 5
 
0.3%
I 5
 
0.3%
Other values (4) 6
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 887
32.4%
4 480
17.5%
2 394
14.4%
5 200
 
7.3%
3 178
 
6.5%
7 153
 
5.6%
8 152
 
5.5%
6 103
 
3.8%
9 99
 
3.6%
0 95
 
3.5%
Other Punctuation
ValueCountFrequency (%)
· 1897
79.6%
. 484
 
20.3%
/ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7635
100.0%
Other Symbol
ValueCountFrequency (%)
413
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19311
59.4%
Common 13174
40.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2442
12.6%
o 2036
10.5%
i 1858
9.6%
n 1771
9.2%
d 1529
7.9%
a 1516
7.9%
b 1480
7.7%
t 1362
 
7.1%
r 1030
 
5.3%
R 854
 
4.4%
Other values (29) 3433
17.8%
Common
ValueCountFrequency (%)
7635
58.0%
· 1897
 
14.4%
1 887
 
6.7%
. 484
 
3.7%
4 480
 
3.6%
413
 
3.1%
2 394
 
3.0%
5 200
 
1.5%
3 178
 
1.4%
7 153
 
1.2%
Other values (6) 453
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30172
92.9%
None 1900
 
5.8%
Misc Symbols 413
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7635
25.3%
e 2442
 
8.1%
o 2036
 
6.7%
i 1858
 
6.2%
n 1771
 
5.9%
d 1529
 
5.1%
a 1516
 
5.0%
b 1480
 
4.9%
t 1362
 
4.5%
r 1030
 
3.4%
Other values (41) 7513
24.9%
None
ValueCountFrequency (%)
· 1897
99.8%
á 2
 
0.1%
ç 1
 
0.1%
Misc Symbols
ValueCountFrequency (%)
413
100.0%

host_id
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct378
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3103244.4
Minimum64036
Maximum3.8005921 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:35.159782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum64036
5-th percentile327674.8
Q1856145
median1916069
Q33910941
95-th percentile4907879.5
Maximum3.8005921 × 108
Range3.7999518 × 108
Interquartile range (IQR)3054796

Descriptive statistics

Standard deviation16968570
Coefficient of variation (CV)5.4680094
Kurtosis490.95491
Mean3103244.4
Median Absolute Deviation (MAD)1283561.5
Skewness22.057688
Sum1.5516222 × 109
Variance2.8793235 × 1014
MonotonicityNot monotonic
2023-11-01T08:28:36.097840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4307081 17
 
3.4%
792218 8
 
1.6%
1429181 7
 
1.4%
1603206 5
 
1.0%
1648634 5
 
1.0%
449677 5
 
1.0%
2694627 4
 
0.8%
406989 4
 
0.8%
474221 4
 
0.8%
4005407 3
 
0.6%
Other values (368) 438
87.6%
ValueCountFrequency (%)
64036 1
0.2%
68997 1
0.2%
70933 2
0.4%
93005 1
0.2%
102840 1
0.2%
110002 1
0.2%
132230 1
0.2%
144943 1
0.2%
149407 1
0.2%
153691 1
0.2%
ValueCountFrequency (%)
380059214 1
0.2%
9709135 1
0.2%
7506316 1
0.2%
6416134 1
0.2%
6005451 1
0.2%
5092388 1
0.2%
5060612 1
0.2%
5020460 1
0.2%
5006415 1
0.2%
5000563 1
0.2%
Distinct303
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:37.116901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length18
Mean length7.182
Min length3

Characters and Unicode

Total characters3591
Distinct characters67
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)42.0%

Sample

1st rowMatthias
2nd rowViviane
3rd rowPatricia Miranda & Paulo
4th rowGoitaca
5th rowMarcio
ValueCountFrequency (%)
19
 
3.0%
nereu 17
 
2.7%
a 17
 
2.7%
maria 14
 
2.2%
e 10
 
1.6%
renata 8
 
1.3%
ricardo 8
 
1.3%
levy 8
 
1.3%
casa 8
 
1.3%
monica 7
 
1.1%
Other values (311) 519
81.7%
2023-11-01T08:28:38.345978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 550
15.3%
e 314
 
8.7%
i 309
 
8.6%
r 225
 
6.3%
o 220
 
6.1%
n 208
 
5.8%
l 198
 
5.5%
138
 
3.8%
s 124
 
3.5%
u 117
 
3.3%
Other values (57) 1188
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2806
78.1%
Uppercase Letter 611
 
17.0%
Space Separator 138
 
3.8%
Other Punctuation 18
 
0.5%
Decimal Number 8
 
0.2%
Nonspacing Mark 4
 
0.1%
Other Symbol 4
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 550
19.6%
e 314
11.2%
i 309
11.0%
r 225
8.0%
o 220
 
7.8%
n 208
 
7.4%
l 198
 
7.1%
s 124
 
4.4%
u 117
 
4.2%
t 99
 
3.5%
Other values (21) 442
15.8%
Uppercase Letter
ValueCountFrequency (%)
M 63
 
10.3%
A 63
 
10.3%
R 57
 
9.3%
C 53
 
8.7%
L 46
 
7.5%
S 36
 
5.9%
J 33
 
5.4%
E 31
 
5.1%
G 31
 
5.1%
N 30
 
4.9%
Other values (15) 168
27.5%
Other Punctuation
ValueCountFrequency (%)
& 11
61.1%
/ 5
27.8%
, 1
 
5.6%
' 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
4 4
50.0%
8 4
50.0%
Space Separator
ValueCountFrequency (%)
138
100.0%
Nonspacing Mark
ValueCountFrequency (%)
4
100.0%
Other Symbol
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3417
95.2%
Common 170
 
4.7%
Inherited 4
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 550
16.1%
e 314
 
9.2%
i 309
 
9.0%
r 225
 
6.6%
o 220
 
6.4%
n 208
 
6.1%
l 198
 
5.8%
s 124
 
3.6%
u 117
 
3.4%
t 99
 
2.9%
Other values (46) 1053
30.8%
Common
ValueCountFrequency (%)
138
81.2%
& 11
 
6.5%
/ 5
 
2.9%
4 4
 
2.4%
4
 
2.4%
8 4
 
2.4%
, 1
 
0.6%
( 1
 
0.6%
' 1
 
0.6%
) 1
 
0.6%
Inherited
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3555
99.0%
None 28
 
0.8%
VS 4
 
0.1%
Dingbats 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 550
15.5%
e 314
 
8.8%
i 309
 
8.7%
r 225
 
6.3%
o 220
 
6.2%
n 208
 
5.9%
l 198
 
5.6%
138
 
3.9%
s 124
 
3.5%
u 117
 
3.3%
Other values (47) 1152
32.4%
None
ValueCountFrequency (%)
é 11
39.3%
á 6
21.4%
ã 4
 
14.3%
É 3
 
10.7%
ç 1
 
3.6%
â 1
 
3.6%
í 1
 
3.6%
ê 1
 
3.6%
VS
ValueCountFrequency (%)
4
100.0%
Dingbats
ValueCountFrequency (%)
4
100.0%

neighbourhood_group
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.0 KiB

neighbourhood
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Copacabana
191 
Ipanema
70 
Santa Teresa
46 
Botafogo
26 
Leblon
25 
Other values (36)
142 

Length

Max length24
Median length17
Mean length9.336
Min length3

Characters and Unicode

Total characters4668
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)2.8%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowIpanema
5th rowCopacabana

Common Values

ValueCountFrequency (%)
Copacabana 191
38.2%
Ipanema 70
 
14.0%
Santa Teresa 46
 
9.2%
Botafogo 26
 
5.2%
Leblon 25
 
5.0%
Barra da Tijuca 22
 
4.4%
Leme 14
 
2.8%
Laranjeiras 10
 
2.0%
Gávea 9
 
1.8%
Centro 8
 
1.6%
Other values (31) 79
15.8%

Length

2023-11-01T08:28:38.773004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
copacabana 191
30.3%
ipanema 70
 
11.1%
santa 46
 
7.3%
teresa 46
 
7.3%
da 27
 
4.3%
botafogo 26
 
4.1%
tijuca 26
 
4.1%
leblon 25
 
4.0%
barra 22
 
3.5%
leme 14
 
2.2%
Other values (45) 137
21.7%

Most occurring characters

ValueCountFrequency (%)
a 1316
28.2%
n 388
 
8.3%
o 364
 
7.8%
e 284
 
6.1%
p 266
 
5.7%
c 240
 
5.1%
b 216
 
4.6%
C 209
 
4.5%
r 166
 
3.6%
130
 
2.8%
Other values (35) 1089
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3939
84.4%
Uppercase Letter 599
 
12.8%
Space Separator 130
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1316
33.4%
n 388
 
9.9%
o 364
 
9.2%
e 284
 
7.2%
p 266
 
6.8%
c 240
 
6.1%
b 216
 
5.5%
r 166
 
4.2%
m 109
 
2.8%
t 105
 
2.7%
Other values (17) 485
 
12.3%
Uppercase Letter
ValueCountFrequency (%)
C 209
34.9%
T 77
 
12.9%
I 72
 
12.0%
B 62
 
10.4%
S 53
 
8.8%
L 53
 
8.8%
G 16
 
2.7%
V 13
 
2.2%
J 12
 
2.0%
F 8
 
1.3%
Other values (7) 24
 
4.0%
Space Separator
ValueCountFrequency (%)
130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4538
97.2%
Common 130
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1316
29.0%
n 388
 
8.6%
o 364
 
8.0%
e 284
 
6.3%
p 266
 
5.9%
c 240
 
5.3%
b 216
 
4.8%
C 209
 
4.6%
r 166
 
3.7%
m 109
 
2.4%
Other values (34) 980
21.6%
Common
ValueCountFrequency (%)
130
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4628
99.1%
None 40
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1316
28.4%
n 388
 
8.4%
o 364
 
7.9%
e 284
 
6.1%
p 266
 
5.7%
c 240
 
5.2%
b 216
 
4.7%
C 209
 
4.5%
r 166
 
3.6%
130
 
2.8%
Other values (29) 1049
22.7%
None
ValueCountFrequency (%)
á 21
52.5%
â 6
 
15.0%
ó 5
 
12.5%
ã 4
 
10.0%
ú 2
 
5.0%
ç 2
 
5.0%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct464
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.965474
Minimum-23.03154
Maximum-22.84008
Zeros0
Zeros (%)0.0%
Negative500
Negative (%)100.0%
Memory size4.0 KiB
2023-11-01T08:28:39.254034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-23.03154
5-th percentile-23.002984
Q1-22.982948
median-22.97355
Q3-22.955032
95-th percentile-22.915426
Maximum-22.84008
Range0.19146
Interquartile range (IQR)0.027915

Descriptive statistics

Standard deviation0.027651223
Coefficient of variation (CV)-0.0012040345
Kurtosis1.768627
Mean-22.965474
Median Absolute Deviation (MAD)0.0106
Skewness1.120192
Sum-11482.737
Variance0.00076459011
MonotonicityNot monotonic
2023-11-01T08:28:39.683060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.97995 3
 
0.6%
-22.98253 3
 
0.6%
-22.96633 3
 
0.6%
-22.98069 3
 
0.6%
-22.98251 3
 
0.6%
-22.96102 2
 
0.4%
-22.98434 2
 
0.4%
-22.9814015 2
 
0.4%
-22.98114 2
 
0.4%
-22.98632 2
 
0.4%
Other values (454) 475
95.0%
ValueCountFrequency (%)
-23.03154 1
0.2%
-23.01805 1
0.2%
-23.0165 1
0.2%
-23.01545 1
0.2%
-23.01524 1
0.2%
-23.01522 1
0.2%
-23.01469 1
0.2%
-23.01344 1
0.2%
-23.01147 1
0.2%
-23.01124 1
0.2%
ValueCountFrequency (%)
-22.84008 1
0.2%
-22.84087 1
0.2%
-22.84208 1
0.2%
-22.89141 1
0.2%
-22.89315 1
0.2%
-22.8957679 1
0.2%
-22.90022 1
0.2%
-22.90544 1
0.2%
-22.90559 1
0.2%
-22.90574 1
0.2%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.211477
Minimum-43.61203
Maximum-43.16542
Zeros0
Zeros (%)0.0%
Negative500
Negative (%)100.0%
Memory size4.0 KiB
2023-11-01T08:28:40.091086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-43.61203
5-th percentile-43.364044
Q1-43.206338
median-43.191465
Q3-43.184337
95-th percentile-43.174899
Maximum-43.16542
Range0.44661
Interquartile range (IQR)0.02200038

Descriptive statistics

Standard deviation0.058951986
Coefficient of variation (CV)-0.0013642669
Kurtosis10.401737
Mean-43.211477
Median Absolute Deviation (MAD)0.009125
Skewness-3.0903823
Sum-21605.738
Variance0.0034753366
MonotonicityNot monotonic
2023-11-01T08:28:40.463171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.19277 3
 
0.6%
-43.19054 3
 
0.6%
-43.17754 3
 
0.6%
-43.19024 3
 
0.6%
-43.18969 3
 
0.6%
-43.19215 3
 
0.6%
-43.18863 2
 
0.4%
-43.19411917 2
 
0.4%
-43.17257 2
 
0.4%
-43.18703 2
 
0.4%
Other values (457) 474
94.8%
ValueCountFrequency (%)
-43.61203 1
0.2%
-43.49074 1
0.2%
-43.47979 1
0.2%
-43.47437 1
0.2%
-43.45943 1
0.2%
-43.45271 1
0.2%
-43.44626 1
0.2%
-43.44438 1
0.2%
-43.43045 1
0.2%
-43.42651 1
0.2%
ValueCountFrequency (%)
-43.16542 1
0.2%
-43.16666 1
0.2%
-43.166691 1
0.2%
-43.16717 1
0.2%
-43.16751 1
0.2%
-43.1676 1
0.2%
-43.168194 1
0.2%
-43.16858 1
0.2%
-43.16868 1
0.2%
-43.17022 1
0.2%

room_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Entire home/apt
339 
Private room
160 
Shared room
 
1

Length

Max length15
Median length15
Mean length14.032
Min length11

Characters and Unicode

Total characters7016
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 339
67.8%
Private room 160
32.0%
Shared room 1
 
0.2%

Length

2023-11-01T08:28:41.178153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T08:28:41.698184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
entire 339
33.9%
home/apt 339
33.9%
room 161
16.1%
private 160
16.0%
shared 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 839
12.0%
t 838
11.9%
o 661
9.4%
r 661
9.4%
a 500
 
7.1%
500
 
7.1%
m 500
 
7.1%
i 499
 
7.1%
h 340
 
4.8%
p 339
 
4.8%
Other values (7) 1339
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5677
80.9%
Space Separator 500
 
7.1%
Uppercase Letter 500
 
7.1%
Other Punctuation 339
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 839
14.8%
t 838
14.8%
o 661
11.6%
r 661
11.6%
a 500
8.8%
m 500
8.8%
i 499
8.8%
h 340
6.0%
p 339
6.0%
n 339
6.0%
Other values (2) 161
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
E 339
67.8%
P 160
32.0%
S 1
 
0.2%
Space Separator
ValueCountFrequency (%)
500
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6177
88.0%
Common 839
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 839
13.6%
t 838
13.6%
o 661
10.7%
r 661
10.7%
a 500
8.1%
m 500
8.1%
i 499
8.1%
h 340
5.5%
p 339
5.5%
E 339
5.5%
Other values (5) 661
10.7%
Common
ValueCountFrequency (%)
500
59.6%
/ 339
40.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 839
12.0%
t 838
11.9%
o 661
9.4%
r 661
9.4%
a 500
 
7.1%
500
 
7.1%
m 500
 
7.1%
i 499
 
7.1%
h 340
 
4.8%
p 339
 
4.8%
Other values (7) 1339
19.1%

price
Real number (ℝ)

Distinct277
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.614
Minimum57
Maximum25000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:42.030261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile119.95
Q1194
median298
Q3542
95-th percentile1716
Maximum25000
Range24943
Interquartile range (IQR)348

Descriptive statistics

Standard deviation1413.581
Coefficient of variation (CV)2.3733173
Kurtosis185.2703
Mean595.614
Median Absolute Deviation (MAD)133.5
Skewness11.866752
Sum297807
Variance1998211.3
MonotonicityNot monotonic
2023-11-01T08:28:42.560237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 15
 
3.0%
250 14
 
2.8%
180 13
 
2.6%
300 11
 
2.2%
500 10
 
2.0%
130 9
 
1.8%
400 9
 
1.8%
220 8
 
1.6%
150 8
 
1.6%
140 7
 
1.4%
Other values (267) 396
79.2%
ValueCountFrequency (%)
57 1
0.2%
59 1
0.2%
60 2
0.4%
65 1
0.2%
70 1
0.2%
75 1
0.2%
77 1
0.2%
79 1
0.2%
90 1
0.2%
97 1
0.2%
ValueCountFrequency (%)
25000 1
0.2%
10000 1
0.2%
8607 1
0.2%
7381 1
0.2%
5874 1
0.2%
4904 2
0.4%
4000 1
0.2%
3464 1
0.2%
3448 1
0.2%
3000 1
0.2%

minimum_nights
Real number (ℝ)

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.934
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:42.870263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum90
Range89
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.4330718
Coefficient of variation (CV)1.6352496
Kurtosis88.592348
Mean3.934
Median Absolute Deviation (MAD)1
Skewness8.3461493
Sum1967
Variance41.384413
MonotonicityNot monotonic
2023-11-01T08:28:43.139317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 135
27.0%
3 128
25.6%
1 80
16.0%
4 59
11.8%
5 47
 
9.4%
7 19
 
3.8%
6 9
 
1.8%
10 4
 
0.8%
14 3
 
0.6%
28 3
 
0.6%
Other values (8) 13
 
2.6%
ValueCountFrequency (%)
1 80
16.0%
2 135
27.0%
3 128
25.6%
4 59
11.8%
5 47
 
9.4%
6 9
 
1.8%
7 19
 
3.8%
8 2
 
0.4%
10 4
 
0.8%
14 3
 
0.6%
ValueCountFrequency (%)
90 1
 
0.2%
60 2
0.4%
30 3
0.6%
28 3
0.6%
22 1
 
0.2%
21 1
 
0.2%
20 1
 
0.2%
15 2
0.4%
14 3
0.6%
10 4
0.8%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct189
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.968
Minimum0
Maximum611
Zeros52
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:43.453347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median32
Q3106
95-th percentile277.25
Maximum611
Range611
Interquartile range (IQR)101

Descriptive statistics

Standard deviation98.80293
Coefficient of variation (CV)1.3540584
Kurtosis5.5996932
Mean72.968
Median Absolute Deviation (MAD)31
Skewness2.1711543
Sum36484
Variance9762.019
MonotonicityNot monotonic
2023-11-01T08:28:44.324345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
10.4%
1 25
 
5.0%
4 16
 
3.2%
8 12
 
2.4%
3 12
 
2.4%
6 11
 
2.2%
5 11
 
2.2%
11 10
 
2.0%
2 10
 
2.0%
9 8
 
1.6%
Other values (179) 333
66.6%
ValueCountFrequency (%)
0 52
10.4%
1 25
5.0%
2 10
 
2.0%
3 12
 
2.4%
4 16
 
3.2%
5 11
 
2.2%
6 11
 
2.2%
7 3
 
0.6%
8 12
 
2.4%
9 8
 
1.6%
ValueCountFrequency (%)
611 1
0.2%
577 1
0.2%
540 1
0.2%
458 1
0.2%
446 1
0.2%
424 1
0.2%
421 1
0.2%
420 1
0.2%
416 1
0.2%
403 1
0.2%

last_review
Date

MISSING 

Distinct222
Distinct (%)49.6%
Missing52
Missing (%)10.4%
Memory size4.0 KiB
Minimum2012-02-21 00:00:00
Maximum2023-09-22 00:00:00
2023-11-01T08:28:44.916417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:45.482417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct164
Distinct (%)36.6%
Missing52
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean0.63069196
Minimum0.01
Maximum4.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:45.888442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.1
median0.34
Q30.8925
95-th percentile2.129
Maximum4.58
Range4.57
Interquartile range (IQR)0.7925

Descriptive statistics

Standard deviation0.74072745
Coefficient of variation (CV)1.1744679
Kurtosis4.5132997
Mean0.63069196
Median Absolute Deviation (MAD)0.29
Skewness1.9551403
Sum282.55
Variance0.54867715
MonotonicityNot monotonic
2023-11-01T08:28:46.527484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 19
 
3.8%
0.04 17
 
3.4%
0.01 15
 
3.0%
0.06 13
 
2.6%
0.03 11
 
2.2%
0.09 11
 
2.2%
0.1 10
 
2.0%
0.05 10
 
2.0%
0.08 9
 
1.8%
0.14 8
 
1.6%
Other values (154) 325
65.0%
(Missing) 52
 
10.4%
ValueCountFrequency (%)
0.01 15
3.0%
0.02 19
3.8%
0.03 11
2.2%
0.04 17
3.4%
0.05 10
2.0%
0.06 13
2.6%
0.07 4
 
0.8%
0.08 9
1.8%
0.09 11
2.2%
0.1 10
2.0%
ValueCountFrequency (%)
4.58 1
0.2%
4.07 1
0.2%
3.79 1
0.2%
3.29 1
0.2%
3.25 1
0.2%
3.14 1
0.2%
3.1 1
0.2%
3.06 1
0.2%
3.04 1
0.2%
2.98 1
0.2%
Distinct19
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.28
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:46.941507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile14.2
Maximum121
Range120
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.09803
Coefficient of variation (CV)2.2912935
Kurtosis38.842185
Mean5.28
Median Absolute Deviation (MAD)1
Skewness5.5727373
Sum2640
Variance146.36232
MonotonicityNot monotonic
2023-11-01T08:28:47.492539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 206
41.2%
2 88
17.6%
3 57
 
11.4%
4 32
 
6.4%
5 22
 
4.4%
6 20
 
4.0%
8 19
 
3.8%
52 17
 
3.4%
7 10
 
2.0%
10 10
 
2.0%
Other values (9) 19
 
3.8%
ValueCountFrequency (%)
1 206
41.2%
2 88
17.6%
3 57
 
11.4%
4 32
 
6.4%
5 22
 
4.4%
6 20
 
4.0%
7 10
 
2.0%
8 19
 
3.8%
9 6
 
1.2%
10 10
 
2.0%
ValueCountFrequency (%)
121 2
 
0.4%
52 17
3.4%
34 3
 
0.6%
28 1
 
0.2%
20 1
 
0.2%
18 1
 
0.2%
14 1
 
0.2%
12 1
 
0.2%
11 3
 
0.6%
10 10
2.0%

availability_365
Real number (ℝ)

ZEROS 

Distinct228
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.116
Minimum0
Maximum365
Zeros55
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:48.047611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q192.75
median224
Q3343.25
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)250.5

Descriptive statistics

Standard deviation130.73427
Coefficient of variation (CV)0.62220045
Kurtosis-1.3839054
Mean210.116
Median Absolute Deviation (MAD)123.5
Skewness-0.28353473
Sum105058
Variance17091.449
MonotonicityNot monotonic
2023-11-01T08:28:48.514605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
11.0%
365 46
 
9.2%
363 13
 
2.6%
364 9
 
1.8%
362 9
 
1.8%
359 7
 
1.4%
327 5
 
1.0%
349 5
 
1.0%
328 4
 
0.8%
286 4
 
0.8%
Other values (218) 343
68.6%
ValueCountFrequency (%)
0 55
11.0%
2 1
 
0.2%
3 1
 
0.2%
5 2
 
0.4%
6 1
 
0.2%
7 1
 
0.2%
15 1
 
0.2%
19 1
 
0.2%
20 2
 
0.4%
21 1
 
0.2%
ValueCountFrequency (%)
365 46
9.2%
364 9
 
1.8%
363 13
 
2.6%
362 9
 
1.8%
361 4
 
0.8%
360 4
 
0.8%
359 7
 
1.4%
358 3
 
0.6%
357 3
 
0.6%
356 1
 
0.2%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.51
Minimum0
Maximum68
Zeros166
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-11-01T08:28:49.006670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q312.25
95-th percentile34.05
Maximum68
Range68
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation12.166592
Coefficient of variation (CV)1.4296817
Kurtosis3.840377
Mean8.51
Median Absolute Deviation (MAD)3
Skewness1.9305516
Sum4255
Variance148.02595
MonotonicityNot monotonic
2023-11-01T08:28:49.513667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 166
33.2%
1 40
 
8.0%
2 35
 
7.0%
4 24
 
4.8%
3 21
 
4.2%
6 15
 
3.0%
8 14
 
2.8%
7 13
 
2.6%
10 12
 
2.4%
5 12
 
2.4%
Other values (40) 148
29.6%
ValueCountFrequency (%)
0 166
33.2%
1 40
 
8.0%
2 35
 
7.0%
3 21
 
4.2%
4 24
 
4.8%
5 12
 
2.4%
6 15
 
3.0%
7 13
 
2.6%
8 14
 
2.8%
9 10
 
2.0%
ValueCountFrequency (%)
68 1
 
0.2%
62 1
 
0.2%
61 1
 
0.2%
60 1
 
0.2%
58 1
 
0.2%
50 1
 
0.2%
48 2
0.4%
45 1
 
0.2%
43 3
0.6%
40 2
0.4%

license
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.0 KiB

Interactions

2023-11-01T08:28:27.086287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:47.573854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:51.010109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:55.791361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:59.655599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:03.274822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:06.823038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:10.109278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:13.956478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:17.729710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:22.149982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:27.439341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:47.807870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:51.486096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:56.243389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:59.876650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:03.532871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:07.060054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:10.378278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:14.377503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:18.316746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:22.590008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:27.740326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:48.255898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:51.778114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:56.571409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:00.255636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:03.808853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:07.463079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:10.780284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:14.821531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:18.602799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:23.041037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:28.034343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:48.597918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:52.330148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:56.979432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:00.747664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:04.187877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:07.766133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:11.250312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:15.188553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:18.924782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:23.406060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:28.427368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:48.851933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:52.743174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:57.342456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:00.986680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:04.581900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:08.040113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:11.524363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:15.513574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:19.172798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:23.707079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:28.842392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:49.153953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:53.582224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:57.658475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:01.214694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:04.890921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:08.326132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:12.035360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:15.739587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:19.558821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:24.358118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:29.155413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:49.469972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:53.957247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:57.943493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:01.485712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:05.268944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:08.668153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:12.338378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:16.006638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:19.897844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:24.936153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:29.520437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:49.843994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:54.272266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:58.305514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:01.957741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:05.515958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:08.922205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:12.662399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:16.270622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:20.225863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:25.476187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:29.882457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:50.114010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:54.708293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:58.674537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:02.247758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:05.763974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:09.249188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:12.947415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:16.496663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:20.499879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:25.841208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:30.229478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:50.439033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:55.060316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:59.024559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:02.539775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:06.073993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:09.558207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:13.218432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:16.977663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:21.409936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:26.306237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:30.610501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:50.728049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:55.362333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:27:59.346580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:02.937800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:06.432016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:09.820224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:13.674459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:17.381689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:21.858964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-01T08:28:26.761264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-01T08:28:49.928689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmneighbourhoodroom_type
id1.0000.7500.039-0.0310.0640.039-0.167-0.1040.018-0.003-0.1390.0510.000
host_id0.7501.0000.060-0.0410.047-0.063-0.178-0.115-0.0510.047-0.1460.5020.000
latitude0.0390.0601.0000.541-0.297-0.105-0.190-0.165-0.115-0.018-0.1710.7870.233
longitude-0.031-0.0410.5411.000-0.2590.0870.1500.048-0.003-0.0420.1220.8410.000
price0.0640.047-0.297-0.2591.0000.042-0.225-0.163-0.1440.217-0.2350.0000.000
minimum_nights0.039-0.063-0.1050.0870.0421.000-0.030-0.122-0.104-0.097-0.0580.0000.000
number_of_reviews-0.167-0.178-0.1900.150-0.225-0.0301.0000.9660.104-0.2140.8150.0000.161
reviews_per_month-0.104-0.115-0.1650.048-0.163-0.1220.9661.0000.043-0.2770.7950.1480.139
calculated_host_listings_count0.018-0.051-0.115-0.003-0.144-0.1040.1040.0431.0000.0910.0840.0570.000
availability_365-0.0030.047-0.018-0.0420.217-0.097-0.214-0.2770.0911.000-0.1590.0000.185
number_of_reviews_ltm-0.139-0.146-0.1710.122-0.235-0.0580.8150.7950.084-0.1591.0000.0000.177
neighbourhood0.0510.5020.7870.8410.0000.0000.0000.1480.0570.0000.0001.0000.731
room_type0.0000.0000.2330.0000.0000.0000.1610.1390.0000.1850.1770.7311.000

Missing values

2023-11-01T08:28:31.175536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-01T08:28:31.879580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-01T08:28:32.246603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
017878Condo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath68997MatthiasNaNCopacabana-22.965990-43.179400Entire home/apt27953012023-09-111.87126525NaN
125026Rental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed · 1 bath102840VivianeNaNCopacabana-22.977350-43.191050Entire home/apt33022722023-09-071.68120324NaN
235764Loft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 baths153691Patricia Miranda & PauloNaNCopacabana-22.981070-43.191360Entire home/apt19234462023-09-112.8214637NaN
348305Rental unit in Ipanema · ★4.74 · 6 bedrooms · 7 beds · 7 baths70933GoitacaNaNIpanema-22.985910-43.203020Entire home/apt344821522023-09-100.99930630NaN
448901Rental unit in Rio · ★4.37 · 4 bedrooms · 5 beds · 3 baths222884MarcioNaNCopacabana-22.965740-43.175140Entire home/apt7033202023-09-100.20130712NaN
549179Rental unit in Rio de Janeiro · ★4.81 · 1 bedroom · 1 bed · 1 bath224192DavidNaNCopacabana-22.979100-43.190080Entire home/apt20141472023-09-051.122015920NaN
651703Rental unit in Rio de Janeiro · ★4.75 · Studio · 1 bed · 1 bath238091DáliaNaNCopacabana-22.981731-43.190571Entire home/apt17832502023-08-281.85232130NaN
753533Home in Joatinga · ★4.94 · 4 bedrooms · 6 beds · 4 baths249439Sherri & AndreNaNJoá-23.008090-43.291130Entire home/apt12782342023-07-240.2413418NaN
860718Rental unit in Rio de Janeiro · ★4.78 · 4 bedrooms · 4 beds · 2 baths292870TâniaNaNFlamengo-22.929720-43.174880Entire home/apt6906102023-02-270.0613282NaN
964795Rental unit in Rio de Janeiro · ★4.75 · 3 bedrooms · 4 beds · 2.5 baths93005AndreaNaNIpanema-22.981690-43.202800Entire home/apt5433642023-08-150.42115622NaN
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
490930736Rental unit in Rio de Janeiro · ★5.0 · 3 bedrooms · 2 beds · 2 shared baths4710077GabriellaNaNSanta Teresa-22.915897-43.182817Private room104732023-03-010.192591NaN
491931638Rental unit in Rio De Janeiro · ★4.76 · 3 bedrooms · 8 beds · 2.5 baths1982737EstadiaNaNLeblon-22.981310-43.220620Entire home/apt6141542023-09-180.421211314NaN
492932270Rental unit in Rio de Janeiro · ★4.66 · Studio · 2 beds · 1 bath5020460SamanthaNaNLeme-22.963850-43.171140Entire home/apt3052782022-05-240.644970NaN
493935028Loft in Copacabana · ★4.83 · 1 bedroom · 1 bed · 1 bath4986394RenataNaNCopacabana-22.978222-43.189121Entire home/apt33622182023-07-271.7149322NaN
494935090Rental unit in Copacabana · ★4.79 · 1 bedroom · 2 beds · 1 bath4986394RenataNaNCopacabana-22.975700-43.192520Entire home/apt2262822023-08-280.67417418NaN
495935162Rental unit in Rio de Janeiro · ★4.40 · 1 bedroom · 6 beds · 1 shared bath4530533VanusaNaNPenha Circular-22.840870-43.297820Private room90152022-09-110.04300NaN
496937388Rental unit in Rio de Janeiro · ★4.46 · 1 bedroom · 2 beds · 1 bath5060612GabrielNaNCopacabana-22.967520-43.182430Entire home/apt16643382023-09-222.63332135NaN
497940749Rental unit in Rio de Janeiro · ★4.50 · 1 bedroom · 1.5 baths64036TatianaNaNSanta Teresa-22.918870-43.179810Private room160562014-09-060.0522620NaN
498941735Rental unit in Rio de Janeiro · 2 bedrooms · 2 beds · 1 bath5092388AnneNaNTijuca-22.920250-43.231940Entire home/apt200010NoneNaN13650NaN
499942218Rental unit in Rio de Janeiro · ★4.80 · 2 bedrooms · 4 beds · 2 baths497736JoanaNaNRecreio dos Bandeirantes-23.007250-43.444380Entire home/apt3002102023-05-010.0813193NaN